code: def __call__(self, y, half_halo, explicit_nhwc, H_split):
      channels_last = not explicit_nhwc and y.is_contiguous(
          memory_format=torch.channels_last
      )
      if explicit_nhwc:
          N, H, W, C = list(y.shape)
          if H_split:
              padded_shape = [N, H + 2 * half_halo, W, C]
              ypad = torch.empty(
                  shape=padded_shape,
                  dtype=y.dtype,
                  device=y.device,
                  memory_format=torch.contiguous_format,
              )
              yleft = ypad[:, :half_halo, :, :]
              ymid = ypad[:, half_halo : H + half_halo, :, :]
              yright = ypad[:, H + half_halo : H + 2 * half_halo, :, :]
              oleft = y[:, :half_halo, :, :]
              oright = y[:, H - half_halo :, :, :]
          else:
              padded_shape = [N, H, W + 2 * half_halo, C]
              ypad = torch.empty(
                  shape=padded_shape,
                  dtype=y.dtype,
                  device=y.device,
                  memory_format=torch.contiguous_format,
              )
              yleft = ypad[:, :, :half_halo, :]
              ymid = ypad[:, :, half_halo : W + half_halo, :]
              yright = ypad[:, :, W + half_halo : W + 2 * half_halo, :]
              oleft = y[:, :, :half_halo, :]
              oright = y[:, :, W - half_halo :, :]
      else:
          N, C, H, W = list(y.shape)
          if H_split:
              padded_shape = [N, C, H + 2 * half_halo, W]
              ypad = torch.empty(
                  shape=padded_shape,
                  dtype=y.dtype,
                  device=y.device,
                  memory_format=torch.channels_last,
              )
              yleft = ypad[:, :, :half_halo, :]
              ymid = ypad[:, :, half_halo : H + half_halo, :]
              yright = ypad[:, :, H + half_halo : H + 2 * half_halo, :]
              oleft = y[:, :, :half_halo, :]
              oright = y[:, :, H - half_halo :, :]
          else:
              padded_shape = [N, C, H, W + 2 * half_halo]
              ypad = torch.empty(
                  shape=padded_shape,
                  dtype=y.dtype,
                  device=y.device,
                  memory_format=torch.channels_last,
              )
              yleft = ypad[:, :, :, :half_halo]
              ymid = ypad[:, :, :, half_halo : W + half_halo]
              yright = ypad[:, :, :, W + half_halo : W + 2 * half_halo]
              oleft = y[:, :, :, :half_halo]
              oright = y[:, :, :, W - half_halo :]
      with torch.cuda.stream(self.stream1):
          self.halo_ex(oleft, oright, yleft, yright)
      with torch.cuda.stream(self.stream2):
          ymid.copy_(y)
      return ypad
  
imports: import torch
  import torch.distributed
  from torch import nn
  import nccl_p2p_cuda
  import peer_memory_cuda
label_desc: Use image node_id 2 for calling the HaloPadder obj's underlying member method code with example usage: obj.__call__(y, half_halo, explicit_nhwc, H_split) and returns: ypad